(working title)
Esben Lykke, PhD student
09 februar, 2023
Purpose…
It was likely a dead end from the get-go :(
Basic Features
ACC derived features1
Sensor-Independent Features2
Forger, Jewett, and Kronauer (1999): a so-called cubic van der Pol equation
\[\frac{dx_c}{dt}=\frac{\pi}{12}\begin{cases}\mu(x_c-\frac{4x^3}{3})-x\begin{bmatrix}(\frac{24}{0.99669\tau_x})^2+kB\end{bmatrix}\end{cases}\]
This thing is dependent on ambient light and body temperature!
Walch et al. (2019) incorporated this feature using step counts from the Apple Watch
But as demonstrated by Walch et al. (2019), a simple cosine function does the trick just as well :)
| Performance of the models to predict each class seperately | ||||
| Logistic Regression | Neural Network | Decision Tree | XGboost | |
|---|---|---|---|---|
| In-bed Prediction | ||||
| F1 Score | 90.88% | 93.69% | 93.37% | 93.77% |
| Accuracy | 92.87% | 94.81% | 94.46% | 94.85% |
| Sensitivity | 85.43% | 92.64% | 93.83% | 93.16% |
| Precision | 97.07% | 94.75% | 92.92% | 94.39% |
| Specificity | 98.17% | 96.35% | 94.91% | 96.06% |
| Sleep Prediction | ||||
| F1 Score | 86.57% | 89.59% | 89.34% | 89.62% |
| Accuracy | 90.77% | 92.41% | 92.10% | 92.39% |
| Sensitivity | 84.65% | 92.95% | 94.20% | 93.49% |
| Precision | 88.59% | 86.47% | 84.96% | 86.06% |
| Specificity | 94.09% | 92.12% | 90.96% | 91.79% |
| Performance of the models to predict each combined class | ||||
| Logistic Regression | Neural Network | Decision Tree | XGboost | |
|---|---|---|---|---|
| In-Bed Awake Prediction | ||||
| F1 Score | 15.88% | 25.45% | 26.41% | 27.54% |
| Accuracy | 92.05% | 92.95% | 93.04% | 93.26% |
| Sensitivity | 11.67% | 18.73% | 19.44% | 19.93% |
| Precision | 24.83% | 39.69% | 41.18% | 44.58% |
| Specificity | 97.57% | 98.05% | 98.09% | 98.30% |
| In-Bed Sleep Prediction | ||||
| F1 Score | 86.56% | 89.54% | 89.35% | 89.61% |
| Accuracy | 90.76% | 92.39% | 92.11% | 92.38% |
| Sensitivity | 84.61% | 92.69% | 94.18% | 93.45% |
| Precision | 88.60% | 86.60% | 84.99% | 86.07% |
| Specificity | 94.10% | 92.23% | 90.98% | 91.80% |
| Decision Tree |
Logistic Regression | Neural Network | XGboost | |
|---|---|---|---|---|
| Sleep Period Time | ||||
| Bias | −0.21 | −1.30 | −0.41 | −0.38 |
| LOA Lower | −2.97 | −4.09 | −3.10 | −3.01 |
| LOA Upper | 2.55 | 1.50 | 2.28 | 2.25 |
| Total Sleep Time | ||||
| Bias | 0.05 | −0.60 | −0.04 | −0.01 |
| LOA Lower | −2.19 | −3.05 | −2.35 | −2.29 |
| LOA Upper | 2.30 | 1.85 | 2.28 | 2.26 |
| Sleep Efficiency | ||||
| Bias | 2.78 | 5.86 | 3.54 | 3.46 |
| LOA Lower | −16.50 | −8.42 | −14.62 | −14.45 |
| LOA Upper | 22.06 | 20.15 | 21.71 | 21.37 |
| Latency Until Persistent Sleep | ||||
| Bias | −1.45 | 4.48 | −2.84 | −3.44 |
| LOA Lower | −88.16 | −83.16 | −91.28 | −68.80 |
| LOA Upper | 85.26 | 92.13 | 85.60 | 61.92 |
| Wake After Sleep Onset | ||||
| Bias | −7.81 | −14.52 | −11.52 | −9.86 |
| LOA Lower | −82.85 | −79.63 | −81.09 | −78.44 |
| LOA Upper | 67.22 | 50.58 | 58.06 | 58.72 |
https://github.com/esbenlykke/sleep_study